Real time evaluation and operation of the smart grid using game theory

2011 ◽  
Author(s):  
Michael Swearingen
Keyword(s):  
Game Theory ◽  
Smart Grid ◽  
Real Time ◽  
Time Evaluation

Real-Time Evaluation of Site Characterization to Direct and Conclude Exploration

2001 ◽  
Vol 1755 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Andrew J. Graettinger ◽  
Thanaporn Supriyasilp ◽  
S. Rocky Durrans
Keyword(s):  
Real Time ◽  
Site Characterization ◽  
Time Evaluation

Real-time research lab in the Sundom smart grid pilot

2016 ◽  
Author(s):  
A. Monot ◽  
M. Wahler ◽  
J. Valtari ◽  
M. Rita-Kasari ◽  
J. Nikko
Keyword(s):  
Smart Grid ◽  
Real Time

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

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